Known PET scanners use an array of segmented detectors (e.g., LSO detectors) in a tomographic arrangement to allow imaging. For example, the INVEON™ dedicated PET scanner from Siemens uses an array of sixty-four 20×20 segmented detectors. The matching and calibration of the system electronics to the detector arrays involves three stages. The first stage identifies the individual crystal elements (pixels) from the raw X and Y ADC (analog to digital) values. The second stage calibrates the energy of the events detected from an individual crystal to the 511 KeV photo peak. The third stage corrects the time stamp of the event to ensure that any inherent timing skew has been removed.
The currently known implementation of crystal identification is an expert system type design that looks at the position profile and determines the crystal locations in a manner that is roughly similar to the approach that an unskilled human would undertake. First, raw position profile data (X, Y) is histogrammed into a 512×512 image (e.g., the digitized X, Y value in a block of the array). An initial estimate of the edges of the calibration array image is performed by summing rows and columns of the position profile and locating their edges. All future activity is limited to this area. Next, a grid representing a scaled version of an average block is laid over the position profile. Row optimization is then performed by comparing the estimated pixel positions with the peak locations within individually banded rows and columns. Finally, a hill-climbing algorithm is used to fine-tune the exact location of every crystal by allowing the peaks to move in a limited distance in a direction with a positive gradient. Crystal lookup tables (CLTs) are generated as the result of the crystal identification process. Although this method achieves around 95% accuracy, it involves intense human interactions for crystal lookup table corrections.